Measure or Management?—Resource Use Indicators for Policymakers Based on Microdata by Households

Abstract

Sustainable Development Goal 12 (SDG 12) requires sustainable production and consumption. One indicator named in the SDG for resource use is the (national) material footprint. A method and disaggregated data basis that differentiates the material footprint for production and consumption according to, e.g., sectors, fields of consumption as well as socioeconomic criteria does not yet exist. We present two methods and its results for analyzing resource the consumption of private households based on microdata: (1) an indicator based on representative expenditure data in Germany and (2) an indicator based on survey data from a web tool. By these means, we aim to contribute to monitoring the Sustainable Development Goals, especially the sustainable management and efficient use of natural resources. Indicators based on microdata ensure that indicators can be disaggregated by socioeconomic characteristics like age, sex, income, or geographic location. Results from both methods show a right-skewed distribution of the Material Footprint in Germany and, for instance, an increasing Material Footprint with increasing household income. The methods enable researchers and policymakers to evaluate trends in resource use and to differentiate between lifestyles and along socioeconomic characteristics. This, in turn, would allow us to tailor sustainable consumption policies to household needs and restrictions.

Full text

sustainability
Article
Measure or Management?—Resource Use Indicators
for Policymakers Based on Microdata by Households
Johannes Buhl 1, Christa Liedtke 1,2, Jens Teubler 1, *, Katrin Bienge 1and Nicholas Schmidt 3
1Wuppertal Institut fuer Klima, Umwelt, Energie gGmbH, Division Sustainable Production and
Consumption, Doeppersberg 19, 42103 Wuppertal, Germany; johannesbuhlsonthofen@gmail.com (J.B.);
christa.liedtke@wupperinst.org (C.L.); katrin.bienge@wupperinst.org (K.B.)
2Industrial Design, Folkwang University of the Arts, Klemensborn 39, 45239 Essen, Germany
3Faculty of Management and Economics, Ruhr University Bochum, Universitätsstraße 150,
44801 Bochum, Germany; nicholas.schmidt1995@gmail.com
*Correspondence: jens.teubler@wupperinst.org
Received: 28 September 2018; Accepted: 16 November 2018; Published: 28 November 2018


Abstract:
Sustainable Development Goal 12 (SDG 12) requires sustainable production and consumption.
One indicator named in the SDG for resource use is the (national) material footprint. A method and
disaggregated data basis that differentiates the material footprint for production and consumption
according to, e.g., sectors, fields of consumption as well as socioeconomic criteria does not yet exist.
We present two methods and its results for analyzing resource the consumption of private households
based on microdata: (1) an indicator based on representative expenditure data in Germany and
(2) an indicator based on survey data from a web tool. By these means, we aim to contribute to
monitoring the Sustainable Development Goals, especially the sustainable management and efficient
use of natural resources. Indicators based on microdata ensure that indicators can be disaggregated
by socioeconomic characteristics like age, sex, income, or geographic location. Results from both
methods show a right-skewed distribution of the Material Footprint in Germany and, for instance,
an increasing Material Footprint with increasing household income. The methods enable researchers
and policymakers to evaluate trends in resource use and to differentiate between lifestyles and along
socioeconomic characteristics. This, in turn, would allow us to tailor sustainable consumption policies to
household needs and restrictions.
Keywords:
sustainable production and consumption; resource indicator; sustainable development
goals; material footprint; household consumption; microdata
1. Introduction
Meeting the resource demand of a growing global consumer class increasingly affects the
environment and places a burden on climate and ecosystems [
1
]. Since household consumption
and production for consumer goods are at the core of the present resource-intensive lifestyles, it is
important to analyze the behavior of private households and assist them in transforming their routines
into more sustainable ones. This means providing new technologies, products, and services that
enable, perhaps even stimulate, a resource-friendly life. Production and consumption in this sense form
an interlaced system that can only be thought and developed in an integrated way.
Resource efficiency in the context of sustainable production and consumption is currently gaining
attention on a national and international level. The current trend towards Product Service Systems (PSS)
as an approach for increasing sustainability can contribute to a sustainable way of linking consumption
and production [
2
–
5
]. Several attempts have been made to support the development of low-resource
and socially accepted approaches of integrating production and consumption. Examples for this
Sustainability 2018,10, 4467; doi:10.3390/su10124467 www.mdpi.com/journal/sustainability

Sustainability 2018,10, 4467 2 of 16
are the Consumer Information Program of the 10 Year Framework of Programmes on Sustainable
Consumption and Production (10YFP) as well as the European Union with its Ecodesign Directive [
6
,
7
].
The Sustainable Development Goal 12 (SDG 12) “Ensure sustainable production and consumption
patterns” integrates a wide range of stakeholders into the process of increasing sustainability in
consumption and production [
8
]. However, the ambitious SDGs and their subgoals require appropriate
indicators for measuring the status quo and the progress until 2030. There is a lack of indicators
which are able to provide the necessary differentiation for socioeconomic characteristics like sex, age,
or income [
9
] and fields of consumption like housing or mobility that hampers the process of providing
improved assistance for producers and consumers in implementing more sustainable product-service
systems and production and consumption patterns [10] as advocated by SDG 12.
Germany published its first sustainability strategy in 2002 and reports the progress towards its
goals every four years [
11
]. This strategy includes different indicators for measuring the development
of sustainability in Germany. The latest update from 2016 adopts the framework of the Sustainable
Development Goals (SDGs). Besides this strategy, Germany implemented a National Program on
Sustainable Consumption that aims to identify the relevant fields of action and adequate measures [
12
].
It gives five guiding principles for a sustainable consumption policy, for example enabling consumers to
implement a sustainable way of consumption. In this respect, the German Program on Resource Efficiency
(Progress II) demands the implementation of a National Program on Sustainable Consumption in order to
promote resource efficiency in consumption [
13
]. Therefore the program strives to develop and improve its
set of indicators for a better measurement of the effects of changes in consumption. Behavioral changes in
favor of more resource efficient consumption are still hampered by obstacles such as a lack of information
and personalized feedback applications. The National Program on Sustainable Consumption suggests
providing such information by the use of assisting carbon and resource calculators [12].
Certain routines and social practices in consumption, as well as patterns in production and existing
business models, complicate a change towards a more sustainable behavior [
14
]. Sustainable consumption
requires sustainably designed product service systems and infrastructures [
15
]. It is only possible to shape
both together and step by step. Progress or regression, as well as rebound effects, must be visible and
therefore demonstrable. Indicators play a crucial role in setting up goals and measuring progress in this
regard. They simplify the complex cause-effect chains within our societies, economies, and with our
environment. On a national level, indicators can be used to develop pathways for sustainability or to
identify trends. Scenario developers usually use these goals and indicators to define a target corridor
in comparison to a status quo or business-as-usual. They are but a tool for aggregated measurement of
impacts and not their management.
However, indicators can potentially also be a tool to evaluate and inform in a differentiated
way by depicting and sometimes explaining the differences—with the means of modern societies
almost in real time. This would make them relevant not only politically (programmes and measures),
but also in everyday decision-making situations, whether in a company (products, infrastructures)
or in a household (lifestyles). This can be achieved using microdata (e.g., from online surveys) and
combining it with already existing methods.
Recent research has managed to quantify some levels of sustainable resource use. While the
global material extraction has drastically increased over the last four decades (from 26.7 billion tonnes
in 1970 to 75.6 billion tonnes in 2010) [
16
], material consumption levels in Europe already reached
40 tonnes per capita and more at the beginning of the century [
17
]. By comparison, Lettenmeier et al.
calculated a sustainable level of only 8 tonnes of a Material Footprint (per person and year) [
18
],
using the MIPS concept (Material Input Per unit of Service) This means a reduction by the factor
five, which requires an appropriate consumer policy and education for sustainable consumption
patterns [
19
]. MIPS (developed by Schmidt-Bleek in the 1990s [
20
]) provides micro-economic indicators
for the resource use of households that include the extraction of materials with and without economic
use (e.g., overburden from mining). Its methodology is based on Material-Flow-Accounting and
compatible with similar input indicators such as cumulated energy demand (ced) or cumulated raw

Sustainability 2018,10, 4467 3 of 16
material demand (KRA). Its indicator Material Footprint can also be adapted to the currently suggested
SDG 12 indicator with the same name. Recent methodological developments make use of improved
LCA data [21–25].
Further research in the field also allowed us to differentiate between different household types
(milieus) and their resource use, although limited to small samples of selected households using
a diary approach. It could also be shown that the calculation of Material Footprints for households
is compatible with methods for the calculation of Carbon Footprints, thus allowing us to compare
lifestyles with high resource use but low carbon intensity and vice versa [21,26].
Two tools have developed that aim at supporting consumers in transforming their consumption
patterns and are supposed to develop synergy effects by combining them. Buhl et al. [
10
] developed
a Material Footprint (MF) indicator based on the MIPS concept and microeconomic expenditure data
in Germany. This indicator was already used to analyze the behavior of households in Germany and
the German federal state of North Rhine-Westphalia (NRW) and allows for differentiation between
regions (here, the federal states of Germany and Germany itself), socioeconomic characteristics of
households and between categories of consumption. The other method is based on microdata that is
directly obtained from an online tool. The “Resource Calculator” [
27
] is a free online application that
enables consumers to examine their consumption patterns by calculating their own Material Footprint.
Consumers can also supply information about their socioeconomic characteristics on a voluntary
and anonymous basis (such as age or years of schooling). Thus, the Resource Calculator provides
an anonymized dataset for analyzing the resource use of private households that—in future—could
provide a representative basis for a new indicator of consumption in the future using a consumer
panel. The calculator itself could also be developed further as an interactive tool for real-time decision
making in all-day routines and practices.
The aim of this paper is to contribute to the process of examining the consumption patterns of
households and to provide the means for micro-economic SDG indicators. Using the example of
natural resource use, the authors show how environmental indicators can be differentiated for private
household types and categories of consumption. We posit that using microdata is a viable solution to
distinguish between the age, sex, income, ethnicity, geographic location, and other characteristics as
required by national policies in Germany [
28
–
30
]. We propose to enhance the present highly aggregated
macro-oriented indicator system for SDG 12 with the help of disaggregating microeconomic data
and indicators.
We hypothesize that a micro-based Resource Consumption Indicator could be an adequate tool to
monitor consumer’s Material Footprint and target achievement (measurement but also management).
Differentiating indicators between arrays of consumption and different consumer groups reveal shifts
and changes between arrays and groups that may otherwise stay undetected and camouflaged by
aggregated macro indicators. Additionally, the online tool based Resource Lifestyle Footprint could
help to facilitate achieving the given targets and address the different types of households and lifestyles
appropriately in this process.
We start by introducing the data and methods that we used in Section 2: the Resource
Consumption Indicator (RCI) and the Resource Lifestyle Footprint (RLF). The results are presented in
Section 3, followed by a discussion of limitations and the derived implications in Section 3. In Section 5
(conclusion), we put the results into the context of research and consumer policies.
2. Methodology
The following section describes briefly the methods and data used for calculating the Material
Footprints of both Resource Consumption Indicator and Resource Lifestyle Footprint. The Resource
Consumption Indicator (1) relates top-down resource-intensities of consumption in a country to
expenditures of consumers. The Resource Lifestyle Footprint (2) models the resources of product-services
and their use bottom up. Both approaches account for the amount of extracted abiotic and biotic materials
from nature and relate them annually on a per person or per capita basis.

Sustainability 2018,10, 4467 4 of 16
2.1. The Resource Consumption Indicator (RCI)
The first approach is based on tables on international trade (see Reference [
31
] on multi-regional
footprint analysis). These multi-regional Input-/Output tables (MRIO) allow for the accounting of
globally extracted raw materials (alongside other indicators) for goods consumed within a country
(including imports but excluding exports of an economy). By allocating these goods to the consumption
of households, country-specific resource-intensities are that can be directly linked to country-specific
household expenditures calculated (see also References [
32
,
33
]). This top-down model converts traded
monetary value into the physical material use of households, thus linking the macro-economy with
microdata on the level of households. It provides a holistic view of resource consumption and is
consistent when comparing countries with each other. Thus, it can be used to generate representative
data on the resource use of households in countries and to differentiate levels of resource consumption
depending on socioeconomic characteristics in the microdata. Buhl et al. (2016) [
10
] successfully
applied the resource intensities to household expenditure data for Germany in order to describe the
Material Footprint (sum of globally induced resource extractions) of different households in the federal
state of North-Rhine-Westphalia in Germany itself.
The weakness of this top-down approach is its inability to explain the differences between
household types and their resource consumption sufficiently. The highly aggregated data with respect
to resource intensities based on MRIO tables also does not allow the identification of specific product
and service options for more sustainable measures by households or policies catering towards a more
resource-efficient lifestyle. This is where bottom-up models can help to fill data gaps by focusing on the
most relevant areas of consumption and disaggregating further into different services and products.
To measure private household consumption for the approach of the Resource Consumption
Indicator, data from the German Survey of Household Income and Consumption (EVS) for the years
2003, 2008 and 2013 were used. The EVS is conducted by the Federal Statistical Office, using household
expenditure as a proxy for consumption. The data are structured into eleven main categories and
152 subcategories according to the Classification of Individual Consumption per Purpose (COICOP).
The analysis described here focuses on the eleven main categories. Furthermore, the EVS includes
socioeconomic data and enables a differentiation between different groups of households or individuals,
clustered by characteristics such as age or household net income [10].
Table 1shows the summary statistics for the yearly expenditures of the main categories between
2003 and 2013 in Germany.
Table 1. The overview of selected variables and descriptive statistics of the EVS.
N(Sample Size) Mean Std.Dev. (Standard Deviation)
Variables 2003 2008 2013 2003 2008 2013 2003 2008 2013
Food and beverages 42,744 44,088 42,792 3634.7 3831.69 3825.74 1933.35 2060.8 2100.27
Clothing 42,744 44,088 42,792 1646.34 1514.8 1577.43 1572.12 1569.62 1665.11
Housing 42,744 44,088 42,792 9449.24 9642.25 10,746.5 6337.31 4900.78 5129.06
Furnishing 42,744 44,088 42,792 1900.51 1624.52 1671.71 4191.31 3805.17 3702.8
Health 42,744 44,088 42,792 1332.44 1438.11 1552.15 3822.46 3694.82 4232.48
Transport 42,744 44,088 42,792 4610.35 4687.23 4628.17 11,824.1 10,489.9 11,369.6
Communication 42,744 44,088 42,792 896.51 833.13 821.45 668.83 527.96 554.92
Recreation and Culture 42,744 44,088 42,792 3807.11 3701.09 3575.99 4002.47 4512.23 4743.77
Education 42,744 44,088 42,792 298.38 292.53 272.98 865.56 1046.73 1070.36
Hotels 42,744 44,088 42,792 1477.48 1654.37 1782.77 1890.12 2143.4 2316.07
Miscellaneous 42,744 44,088 42,792 1379.76 1351.69 1297.62 1892.59 2103.99 1948.51
Household size 42,744 44,088 42,792 2.43 2.28 2.10 1.23 1.17 1.09
NRW 9223 7708 7823 1 1 1 0 0 0
Data: German Survey of Household Income and Consumption, 2003, 2008, 2013. Expenditure data in Euro.
“Household size” and “NRW” (i.e., living in the federal state of NRW in Germany) represent socio-demographics of
the sample.
The expenditures of households in the EVS were related to so-called resource intensity factors
(household resource use per Euro). These factors stem from multi-regional input-output analyses

Sustainability 2018,10, 4467 5 of 16
(MRIO) of economy-wide material flow accounts and the continuous household budget surveys for
Germany in the year 2005. Table 2provides an overview of the average resource intensities in the main
consumption categories [34].
Resource intensities allow the measurement of the impact of private consumption on the
environment and can be used to calculate the Material Footprint of consumption. The calculation of
the Material Footprint based on microdata on expenditure (EVS) and respective resource intensities of
the main COICOP categories are described in Appendix A.
The further analysis is based on the differentiation between the main COICOP categories from
“Food and beverages” to “Miscellaneous”. However, Buhl et al. (2016) show an application of the
method that further differentiates within the main COICOP category “Transport” by calculating
resource intensities for specific transport services like local and long distance trains, air travel, or the
use of second-hand cars [10].
Table 2. The resource intensities of private household consumption in Germany.
Consumption Categories Resource Intensity (kg/€)
Food and beverages 5.09
Housing etc. 3.18
Furnishings etc. 2.99
Transport 1.50
Restaurants and hotels 1.40
Health 0.60
Education 0.48
Recreation and culture 0.41
Communication 0.37
Clothing 0.19
Miscellaneous 0.19
Based on Buhl et al., 2016 [10]. Data: Buhl and Acosta 2015 [32].
2.2. The Resource Lifestyle Footprint (RLF)
The “Resource Calculator” tool (see https://www.ressourcen-rechner.de/) provides a footprint
of a household’s lifestyles. It calculates the Material Footprint of products, their services, and usage
directly and over the whole lifecycle of their production, use, and end-of-life (including material
extractions in other countries). It combines quantitative (and often physical) survey data on household
consumption with survey data on socioeconomic characteristics and household attitudes in order to
calculate an individual or lifestyle footprint. This approach allows us to identify drivers and barriers of
resource use and matches socio-demographic characteristics, lifestyle decisions, subjective attitudes or
assessments, social norms, and individual preferences as well as budget restrictions to the individual
footprint or ecological backpack (see References [
35
,
36
] on the concept). Resource use can thus be
reduced not only by consuming resource efficient products, but also by improving the service these
products provide. This bottom-up model has been successfully tested in several studies ([
18
,
21
,
26
]) and
is compliant with the Material Flow Accounting (MFA) and Life Cycle Assessment (LCA) methodology.
It is also compatible with generic databases for lifecycle inventories as well as assessments of output
indicators such as carbon footprints (as shown by References [22,23]).
The calculator generates a growing database because of its permanent online accessibility.
Besides questions regarding the most important fields of consumption like housing and mobility,
users can voluntarily and anonymously provide data concerning their socioeconomic characteristics.
This was surveyed alongside other subjective attitudes and norms such as relative household income
in comparison, subjective health or subjective well-being. Table 3lists the different areas of private
consumption in the Resource Calculator.

Sustainability 2018,10, 4467 6 of 16
Table 3. The description of consumption categories in the Resource Calculator.
Groups of Consumption in Resource Calculator Description of Category
Nutrition diets, food waste, and consumption of foodstuffs and drinks
Housing buildings, heat, and electricity use
Consumer Goods appliances, clothes, furniture
Mobility day-to-day travel with cars, bikes, public transport
Leisure hobbies, sports, cultural activities
Vacation vacation travel and accommodation
The Resource Calculator application was advertised via different channels such as the website of
the Wuppertal Institute, online blogs on sustainable living, and reviews of product testing magazines.
Between the launch on 25 February 2015 and 13 February 2017, 49,037 persons participated without any
incentives. Data preparation and the removal of invalid and implausible responses left a database of 44,514
being analyzed. For a more detailed description, necessary transformations and underlying assumptions
see Buhl et al., 2017 [
27
]. Table 4comprehends the most relevant dimensions and variables surveyed by
the Resource Calculator. Socioeconomic, personal, and household characteristics, as well as subjective
assessments and other lifestyle features, complement disaggregated information on the Material Footprint.
Table 4. The overview of variables and descriptive statistics of the Resource Calculator.
Statistic N Mean Std. Dev. Min Max
Personal characteristics
Female 26,103 0.62 0.49 0 1
Age 24,596 36.00 12.00 18 71
Schooling years 26,118 14.00 3.20 9 21
Occupational status 18,463 3.00 1.10 1 4
Unemployed 18,463 0.14 0.35 0 1
Household characteristics
Household size 44,238 2.20 1.00 1.00 6.00
Number of children 9119 1.60 0.71 1 4
Size of dwelling (m2)30,482 95.00 47.00 7.00 300.00
Subjective assessments
Subjective health 17,297 1.30 0.57 −1 2
Relative income 22,125 −0.41 1.00 −2 2
Life satisfaction 26,041 7.30 1.80 1 10
Social ties satisfaction 17,690 1.00 0.71 −2 2
Lifestyle
Diet 44,317 2.20 0.84 1 4
Vegetarian 44,317 0.33 0.47 0 1
Hobby hours 44,091 8.00 12.00 0.00 75.00
Days on vacation 44,056 15.00 13.00 0 81
Trips (in km) 44,086 220.00 327.00 0.00 1,800.00
Material Footprints (kg)
Housing 44,068 8722.00 4059.00 45 26,804
Consumer goods 44,068 2859.00 1161.00 2 6936
Nutrition 44,068 5160.00 1323.00 82 9145
Leisure 44,069 446.00 639.00 0 5113
Mobility 43,456 6682.00 6407.00 1 39,447
Vacations 44,068 1525.00 1532.00 0 10,200
Overall Material Footprint 44,068 25,897.00 10,041.00 2.711 76,570
Note: Descriptive statistics include the number of observations (N), mean, standard deviation (Std.Dev.),
minimum (Min.) and maximum (Max.) of observations. “Trips” is the distance in km for trips and events
during the past month. “Days on vacation” are days on vacation overall in the past year. “Hobby hour” are the
hours overall spent on hobbies on average per month. “Social ties satisfaction” is the personal evaluation on how
often social relations are perceived as satisfying (as the Likert scale). “Relative income” is the assessment of the
household net income in comparison (as the Likert scale).

Sustainability 2018,10, 4467 7 of 16
3. Results
The following section shows original results as well as results from recent studies on the Resource
Calculator using the methods and data sets described in Section 2.
3.1. Resource Consumption Indicator Based on Microdata EVS and Resource Intensities
The R
CF
was used to monitor the resource use of private households in the sustainability report
of the Ministry for Environment, Agriculture, Conservation, and Consumer Protection of the State of
North Rhine-Westphalia (NRW). One aim was to examine if and to which extent the Resource Indicator
can contribute to the goals and indicators of SDG 12 and how it could be improved. For this purpose,
the EVS data and the resource intensity data described in Section 2.1. were used as a database.
The Material Footprint of private households in NRW accounted for 31 t per capita in 2013.
Using microdata enabled the researchers to further analyze the distribution of the Material Footprint
among households. Figure 1shows a right-skewed distribution although the 99
th
percentile was
removed. This implies a relatively strong bias of the average Material Footprint due to relatively few
households being responsible for relatively high amounts of resource use.
Sustainability 2018, 10, x FOR PEER REVIEW 7 of 16
The following section shows original results as well as results from recent studies on the
Resource Calculator using the methods and data sets described in Section 2.
3.1. Resource Consumption Indicator Based on Microdata EVS and Resource Intensities
The RCF was used to monitor the resource use of private households in the sustainability report
of the Ministry for Environment, Agriculture, Conservation, and Consumer Protection of the State of
North Rhine-Westphalia (NRW). One aim was to examine if and to which extent the Resource
Indicator can contribute to the goals and indicators of SDG 12 and how it could be improved. For this
purpose, the EVS data and the resource intensity data described in Section 2.1. were used as a
database.
The Material Footprint of private households in NRW accounted for 31 t per capita in 2013. Using
microdata enabled the researchers to further analyze the distribution of the Material Footprint among
households. Figure 1 shows a right-skewed distribution although the 99th percentile was removed.
This implies a relatively strong bias of the average Material Footprint due to relatively few
households being responsible for relatively high amounts of resource use.
Figure 1. The distribution of Material Footprint (years 2003, 2008, 2013) according to Buhl et al., 2016
[10].
The application of the indicator of private household data from NRW revealed three categories
that accounted for the highest shares in resource consumption: housing, food, and transport [32,37].
However, smaller shares on household expenditure do not necessarily lead to lower Material
Footprints, as resource intensities can be very different between categories of consumption.
Figure 2 shows the overall change in resource use of private households in NRW between 2003
and 2013. The environmental impact of these relative changes in resource consumption depends on
the share of the categories in the overall Material Footprint. On the one hand, Communication, for
example, exhibits a strong increase of more than 30%, which might come from rapid innovations in
information and communication technologies. On the other hand, transport, for example, exhibits a
Figure 1.
The distribution of Material Footprint (years 2003, 2008, 2013) according to Buhl et al., 2016 [
10
].
The application of the indicator of private household data from NRW revealed three categories
that accounted for the highest shares in resource consumption: housing, food, and transport [
32
,
37
].
However, smaller shares on household expenditure do not necessarily lead to lower Material Footprints,
as resource intensities can be very different between categories of consumption.
Figure 2shows the overall change in resource use of private households in NRW between 2003
and 2013. The environmental impact of these relative changes in resource consumption depends on the
share of the categories in the overall Material Footprint. On the one hand, Communication, for example,
exhibits a strong increase of more than 30%, which might come from rapid innovations in information
and communication technologies. On the other hand, transport, for example, exhibits a decrease in

Sustainability 2018,10, 4467 8 of 16
the Material Footprint. Buhl et al. (2017) differentiate resource intensities in “Transport” and show
that a decrease in its Material Footprint comes from, e.g., a reduction of gas consumption, reduced car
ownership, and “other” reasons. Due to the relatively high resource intensity of transport, a small
decrease in expenditure for transport cancels out a larger increase in expenditure for communication
services and technologies between 2003 and 2013.
Sustainability 2018, 10, x FOR PEER REVIEW 8 of 16
decrease in the Material Footprint. Buhl et al. (2017) differentiate resource intensities in “Transport”
and show that a decrease in its Material Footprint comes from, e.g., a reduction of gas consumption,
reduced car ownership, and “other” reasons. Due to the relatively high resource intensity of
transport, a small decrease in expenditure for transport cancels out a larger increase in expenditure
for communication services and technologies between 2003 and 2013.
Figure 2. The change in the Material Footprint in NRW 2003–2013 according to Buhl et al., 2016 [16].
Data: Buhl and Acosta 2015 [32].
In sum, the total resource use in NRW remained almost unchanged over the three reporting
periods with a reduction of 3.9% between 2003 and 2013 on a comparable high level of resource use.
It is interesting to note that this small change in total is a result of significant shifts between the
different fields of consumption. This implies that consumption patterns in NRW changed, even
though the overall resource use did not by a large margin [16].
3.2. Resource Lifestyle Footprint Based on Survey Data from an Online Web Tool
The overall Material Footprint of users of the Resource Calculator accounts for 26 t per user (and
year). The distribution of the Material Footprint shows a similar right-skewed distribution as
revealed by the Resource Indicator. This corroborates our findings that the Material Footprint of
private households is strongly biased by high resource use of relatively few households.
Figure 3 shows the six categories presented in Table 3 and their shares in the respective Material
Footprints of the deciles. It is notable that some shares, such as food and vacation, remain nearly
constant from the first to the tenth decile while others, such as housing and mobility, increase
strongly. This allows us to conclude that the potential main drivers of a high Material Footprint
appear to be these categories.
Communication
Transport
Education
Restaurants and hotels
Furnishings, household equipment, appliances
Miscellaneous goods and services
Food and non-alcoholic beverages
Housing, water, electricity and other fuels
Clothing and footwear
Recreation and culture
Health
Overall
-10 010 20 30
Change of Material Footprint (%)
Figure 2.
The change in the Material Footprint in NRW 2003–2013 according to Buhl et al., 2016 [
16
].
Data: Buhl and Acosta 2015 [32].
In sum, the total resource use in NRW remained almost unchanged over the three reporting
periods with a reduction of 3.9% between 2003 and 2013 on a comparable high level of resource use.
It is interesting to note that this small change in total is a result of significant shifts between the different
fields of consumption. This implies that consumption patterns in NRW changed, even though the
overall resource use did not by a large margin [16].
3.2. Resource Lifestyle Footprint Based on Survey Data from an Online Web Tool
The overall Material Footprint of users of the Resource Calculator accounts for 26 t per user
(and year). The distribution of the Material Footprint shows a similar right-skewed distribution as
revealed by the Resource Indicator. This corroborates our findings that the Material Footprint of
private households is strongly biased by high resource use of relatively few households.
Figure 3shows the six categories presented in Table 3and their shares in the respective Material
Footprints of the deciles. It is notable that some shares, such as food and vacation, remain nearly
constant from the first to the tenth decile while others, such as housing and mobility, increase strongly.
This allows us to conclude that the potential main drivers of a high Material Footprint appear to be
these categories.

Sustainability 2018,10, 4467 9 of 16
Sustainability 2018, 10, x FOR PEER REVIEW 9 of 16
Figure 3. The Material Footprints for deciles and category shares.
Users have been asked to classify their income in respect to the average household net income
on a symmetric scale from clearly below average to clearly above average. Surveying the relative
household net income makes it easier for users of the calculator to state their net income and to
prevent non-response of users. Again, the results reveal an increasing Material Footprint with
increasing household net income (see Figure 4).
Figure 4. The relative household net income categories and related mean Material Footprint.
The data was also used by Buhl et al., 2017 [27] to examine the relationship between the Material
Footprint and life satisfaction. As postulated by Buhl et al., 2017, the use of natural resources is not
clearly linked to users subjective well-being (see Figure 5).
Figure 3. The Material Footprints for deciles and category shares.
Users have been asked to classify their income in respect to the average household net income
on a symmetric scale from clearly below average to clearly above average. Surveying the relative
household net income makes it easier for users of the calculator to state their net income and to prevent
non-response of users. Again, the results reveal an increasing Material Footprint with increasing
household net income (see Figure 4).
Sustainability 2018, 10, x FOR PEER REVIEW 9 of 16
Figure 3. The Material Footprints for deciles and category shares.
Users have been asked to classify their income in respect to the average household net income
on a symmetric scale from clearly below average to clearly above average. Surveying the relative
household net income makes it easier for users of the calculator to state their net income and to
prevent non-response of users. Again, the results reveal an increasing Material Footprint with
increasing household net income (see Figure 4).
Figure 4. The relative household net income categories and related mean Material Footprint.
The data was also used by Buhl et al., 2017 [27] to examine the relationship between the Material
Footprint and life satisfaction. As postulated by Buhl et al., 2017, the use of natural resources is not
clearly linked to users subjective well-being (see Figure 5).
Figure 4. The relative household net income categories and related mean Material Footprint.
The data was also used by Buhl et al., 2017 [
27
] to examine the relationship between the Material
Footprint and life satisfaction. As postulated by Buhl et al., 2017, the use of natural resources is not
clearly linked to users subjective well-being (see Figure 5).

Sustainability 2018,10, 4467 10 of 16
Sustainability 2018, 10, x FOR PEER REVIEW 10 of 16
Figure 5. The scatter and line prediction plot of the Material Footprint (in kg) vs. life satisfaction (10-
point scale). Confidence band with 𝑎=0.01. Buhl et al., 2017 [27].
To test their hypothesis, Buhl et al., 2017 conducted a stepwise multivariate regression analysis.
They found that the strongest impacts on life satisfaction are measured for subjective health and for
satisfaction with social ties. Real income and gender reveal smaller, but still notable effects. The
influence of age, vacation days, and the Material Footprint is rather weak. Subjective assessments and
norms appear to have the strongest impact on subjective well-being, followed by socio-demographic
characteristics that seem to have less relevance in this context. The Material Footprint has the smallest
impact with a slightly negative effect on life satisfaction [27].
Data from the Resource Calculator allows for the disaggregation of Material Footprints and the
analysis of complex research questions in the realms of empirical consumer research regarding the
link between socioeconomic features and the Material Footprint. In addition, the online web tool
approach allows for a quick and flexible alteration of the variables surveyed and a constant flow of
survey data.
4. Discussion
The results presented in the previous sections are based on two methods to analyze the Material
Footprint of private households according to the requirements of SDG 12. Both concepts have certain
strengths as well as potential weaknesses or limitations.
4.1. Resource Consumption Indicator
Regarding the RCI, limitations are the relatively high data aggregation of the main categories and
the restriction to consumption expenditure as a proxy for consumption.
Using aggregated data limits the differentiation ability in regard to the consumption of products
and services. For instance, one euro invested in the construction of a private house cannot be
differentiated from another one invested in maintaining heating. Both are equally subsumed under
“housing”. Due to this, the depth of analysis of resource use related to certain consumption patterns
is restricted. Lifecycle data could be used to extend the current database by disaggregating resource
intensities for specific products and services. Such an improved disaggregation of data was
successfully conducted by Buhl et al., 2017 for transport and mobility services [37].
Additionally, expenditure data are used as a proxy for consumption. Expenditure data are
available in internationally harmonized, official and representative household statistics, which
Figure 5.
The scatter and line prediction plot of the Material Footprint (in kg) vs. life satisfaction
(10-point scale). Confidence band with a=0.01. Buhl et al., 2017 [27].
To test their hypothesis, Buhl et al., 2017 conducted a stepwise multivariate regression analysis.
They found that the strongest impacts on life satisfaction are measured for subjective health and
for satisfaction with social ties. Real income and gender reveal smaller, but still notable effects.
The influence of age, vacation days, and the Material Footprint is rather weak. Subjective assessments
and norms appear to have the strongest impact on subjective well-being, followed by socio-demographic
characteristics that seem to have less relevance in this context. The Material Footprint has the smallest
impact with a slightly negative effect on life satisfaction [27].
Data from the Resource Calculator allows for the disaggregation of Material Footprints and the
analysis of complex research questions in the realms of empirical consumer research regarding the link
between socioeconomic features and the Material Footprint. In addition, the online web tool approach
allows for a quick and flexible alteration of the variables surveyed and a constant flow of survey data.
4. Discussion
The results presented in the previous sections are based on two methods to analyze the Material
Footprint of private households according to the requirements of SDG 12. Both concepts have certain
strengths as well as potential weaknesses or limitations.
4.1. Resource Consumption Indicator
Regarding the R
CI
, limitations are the relatively high data aggregation of the main categories and
the restriction to consumption expenditure as a proxy for consumption.
Using aggregated data limits the differentiation ability in regard to the consumption of products
and services. For instance, one euro invested in the construction of a private house cannot be
differentiated from another one invested in maintaining heating. Both are equally subsumed under
“housing”. Due to this, the depth of analysis of resource use related to certain consumption patterns
is restricted. Lifecycle data could be used to extend the current database by disaggregating resource
intensities for specific products and services. Such an improved disaggregation of data was successfully
conducted by Buhl et al., 2017 for transport and mobility services [37].
Additionally, expenditure data are used as a proxy for consumption. Expenditure data are
available in internationally harmonized, official and representative household statistics, which ensure
continuous surveys and high data quality. There is a proven correlation between expenditure,
consumption and hence resource use. However, there are other factors influencing the measurable

Sustainability 2018,10, 4467 11 of 16
impact on the environment. Disregarding those factors can cause bias. Examples of such factors are
the households’ repairing behavior, their willingness to decide on second-hand goods, and the way
and intensity of using a certain good. Value conceptions may also lead a household to opt for goods
that are more expensive than comparable alternatives, but cause a similar resource use [16].
4.2. Resource Lifestyle Footprint
The method and data limitations of the R
LF
stem from non-representative sampling on the one
hand and the necessary time efficiency of the survey on the other hand.
Even though the Resource Calculator provides a large amount of user data due to the high usability
and, thus, the acceptance of the calculator tool, the sample includes some bias due to the voluntary
sampling. The share of young female users, vegetarians, and vegans, for example, is disproportionally
high, which indicates a self-selection of pro-environmental users [
27
]. Future studies should aim for
a more representative sampling when using the Resource Calculator as a survey tool. Adding more
detailed questions regarding personal information about the users themselves could increase the
informative value and the representativeness of the database.
The second limitation of the Resource Calculator relates to the requirement to conduct a survey
within a certain amount of time. This results in a limited set of questions that do not allow us to
analyze every aspect of consumer’s consumption patterns. Many products and services were omitted
from the survey (e.g., compared to the “diary” approach in Reference [
26
]), because they would
not contribute much to a higher footprint. Other questions were simplified, aiming at helping the
households to complete the survey rather than asking for precise physical values. Finally, even the
most comprehensive bottom-up survey would exclude certain products and could not account for
every variation of the product types. So there is always some part of the Material Footprint that cannot
be related to households individually. Further analysis of the available footprint data could help to
identify the essential questions, e.g., by means of unsupervised learning and by using an average
pedestal of resource consumption for areas of a low importance (e.g., durable goods such as jewelry or
the use of non-living space).
5. Conclusions
5.1. Summary
We introduced two methods for analyzing the Material Footprint of private households based
on microdata. The first method (resulting in the Resource Consumption Indicator) is based on
expenditure data according to internationally harmonized COICOP. The second method (resulting in
the Resource Lifestyle Footprint) is based on survey data from a web tool called Resource Calculator.
Both methods allow us to differentiate the Material Footprint along arrays of consumption like
housing and mobility as well as socioeconomic characteristics like age or income and thus meet
the disaggregation requirement to SDG indicators. The results from applying the two methods in
Germany shows that the Material Footprint ranges between 26 t and 31 t per capita in Germany and its
distribution is right-skewed. The most relevant categories are housing, mobility, and nutrition. When it
comes to disaggregating the Material Footprint along socioeconomic characteristics, we showed that
an increasing household net income leads to an increasing Material Footprint.
5.2. Methods
Using microdata from the statistical offices for a Resource Indicator offers three main benefits [
32
].
•First, it enables a representative depiction of private household consumption.
•
Second, private household consumption can be examined by looking at differentiated
consumption categories such as energy or food and its shifts and changes for the past decades.

Sustainability 2018,10, 4467 12 of 16
•
Third, private household consumption can be examined by looking at differentiated population
groups (disaggregated for example by income or age).
Analyzing consumption by using resource intensities offers a possibility to evaluate its
environmental impact. The concept relies on a representative, internationally harmonized and thus
comparable data according to COICOP that is available in different countries.
The method used for the Resource Lifestyle Footprint (based on the Resource Calculator) provides
new options for consumers to receive real-time feedback and for researchers to collect and gather data
quickly, flexibly, and constantly over time [
27
]. Further research regarding the impact of socioeconomic
characteristics on resource use could help us to identify appropriate reduction strategies for different
groups of consumers as Lettenmeier 2018 successfully showed [
38
]. Moreover, we strive to collect more
data from users abroad in order to compare the Material Footprints internationally (e.g., in a current
project on sustainable lifestyles in 7 different countries). So far, the sample of users from abroad is too
small to conduct a proper comparative analysis.
Despite some weaknesses, the presented Resource Consumption Indicator appears to be a good
and expandable method for measuring the resource use of private households according to SDG 12.
However, an improved database is crucial for increased reliability. This issue could be addressed by
collecting lifecycle data.
The Resource Lifestyle Footprint is a promising attempt but should be improved regarding the
aforementioned limitations. Especially, it will be important to focus on improving the database to
receive a more representative sample while condensing the questions about resource use to the most
essential ones. In this regard, the survey instrument that indicates the Material Footprint of private
households could be incorporated into existing representative surveys like the Socioeconomic Panel
(SOEP) or GESIS Panel in Germany or equivalent panels on a European level, e.g., the European
Community Household Panel (ECHB). This way, environmental policy evaluation and research
on sustainable consumption would benefit from the longitudinal design of the surveys and link
environmental issues with an extensive set of socioeconomic predictors efficiently. In any case, it would
be helpful to gather more detailed personal and household information to facilitate differentiation
between them.
Combining the presented tools could address some of the aforementioned limitations and further
improve the usability of microdata for measuring progress towards achieving SDG 12. The Resource
Consumption Indicator offers a possibility to measure this progress over time and the status quo.
The Resource Lifestyle Footprint can provide a new and more differentiated micro-level database for
analyzing consumption-related resource use. The combination of both methods (or similar methods
with microdata for that matter) would also enhance scenario building. As the majority of environmental
scenarios currently focus on technological and economic feasibility, there is a lack of scenario models
that also investigate the social and cultural drivers and barriers of sustainable development [39].
5.3. Policy Making
The Resource Consumption Indicator and the Resource Lifestyle Footprint appear to be promising
tools for deepening the understanding of private household consumption, the interaction of production
and consumption patterns, and detecting unused potentials to increase its sustainability according
to the SDGs. First results from applying the tools already revealed insights about the structure of
the resource use of private households. We conclude that microeconomic data offers an important
enhancement of the present macro data-based indicator system. Indicators based on microdata are
able to evaluate and inform in a differentiated and disaggregated way, in perspective even in real
time. As such the methods reveal shifts in resource use between different arrays of consumption and
consumer groups that would otherwise stay undetected and camouflaged by highly aggregated macro
indicators. Policy evaluations benefit from a disaggregated perspective on the Material Footprint of
private household instead of evaluating the overall trend in the Material Footprint. Policymakers
may wonder why efforts to reduce the natural resource in mobility does not show a decreasing

Sustainability 2018,10, 4467 13 of 16
overall Material Footprint, e.g., due to indirect rebound effects and shifts of consumption patterns.
For instance, differentiating the Material Footprint along arrays of consumption allows us to evaluate
whether a reduction of the Material Footprint in mobility is offset by an increase in natural resource
use by housing or communications. As such, policymakers may identify which policies in specific
arrays of consumption may be more effective in reducing the Material Footprint since rebound effects
and shifts of consumption are less pronounced.
A more differentiating approach to indicators is not relevant politically (for (inter)national
policies and programs like the indicator framework of SDGs and national programmes striving to
implement them), but also in everyday decision-making situations, whether in the company (products,
infrastructures) or in the household (lifestyles). In fact, the households themselves may evaluate
whether changes in one array of consumption are offset by shifts of their consumption into other
arrays. For instance, private households may reduce their resource use by foregoing resource-intensive
vacations abroad. At the same time, they may become aware that their savings are offset due to
intensified leisure activities.
Indicators based on microdata (or indicator set for different goals) are fundamental for the
implementation of national policies such as the National Program on Sustainable Consumption in
Germany. They allow us to combine efforts for sustainable lifestyles by companies, households and
policymakers alike. Does a product or service contribute to achieving an SDG? Are certain production
and consumption patterns sustainable and to what extent? Which rebounds can be anticipated? Which
trends evolve and do we leave certain groups behind in doing so? Does a policy instrument support
sustainable development or not? This type of evaluation system would—in the long run and combined
with real-time tools—help to manage and measure sustainable development.
Author Contributions:
J.B. and C.L. drafted the thesis. J.B., C.L., J.T. and K.B. conceptualized the paper. J.B. and
J.T. analyzed the data and wrote the paper. K.B. and N.S. contributed research and reviewed the paper. All authors
draw the conclusions.
Funding: This research received no external funding.
Acknowledgments:
No funding was received in support of this paper. The original research and discussion
on the resource use of households was partly funded by the federal Ministry for the Environment in North
Rhine-Westphalia, Germany (Ministerium für Umwelt, Landwirtschaft, Natur-und Verbraucherschutz des Landes
Nordrhein-Westfalen).
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A. Calculating the Material Footprint Based on Resource Intensities
The Resource Indicator is calculated as the Material Footprint of the consumption of private
households. This Material Footprint is the product resulting from the multiplication of the consumption
expenditure c by the resource intensity r.
Material Footprint =c×r (A1)
The consumption expenditure for the jth consumption category of k consumption categories in
total is calculated as the arithmetic mean of the consumption expenditure of the ith household out of n
households in total in time t (measured in years).
cj=1
n
n
∑
i
ct
ij (A2)
The consumption expenditure is then adjusted for inflation to the base year t by considering the
inflation rate
π
of the subsequent years in the respective consumption category j. This prevents a bias
resulting from inflation.
cj=1
n"n
∑
i
ct
ij−ct+1
ij ×πt+1
j#(A3)

Sustainability 2018,10, 4467 14 of 16
The consumption expenditure of the ith household is put into the context of the household size h
to obtain a per capita result instead of a per household result.
cj=1
n"n
∑
i
[(ct
ij−(ct+1
ij ×πt+1
j))/ht
i## (A4)
To enable a differentiation by different subgroups, Buhl et al. introduce a dimension X,
representing socioeconomic characteristics like household income.
cj=1
n"n
∑
i
[(ct
ijX−(ct+1
ijX ×πt+1
j))/ht
iX## (A5)
The resource intensity is calculated by dividing the total resource use (indicated by household
consumption) Rt
jby the associated consumption expenditure in category j in year t (Equation (A3)).
rj=Rt
j
∑n
ict
ij
(A6)
The Material Footprint can be expressed as the arithmetic mean of the respective Material
Footprints of the jth consumption category out of k consumption categories in total by inserting
Equations (A5) and (A6) into Equation (A1):
Material Footprint =1
kx
1
nx
∑k
j∑n
i[(ct
ijX−(ct+1
ijX ×πt+1
j))/ht
iX]×Rt
j
∑n
ict
ij
(A7)
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2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).